KardiaMobile for detecting atrial fibrillation

The main clinical evidence comprises 27 studies including 5 randomised controlled trials

3.1 There were 27 studies relevant to the decision problem in the scope:

3.2 Of the 27 included studies, 16 studies were peer reviewed, including 4 UK studies (Bray et al. 2021; Reed et al. 2021; Reed et al. 2019; Dimarco et al. 2018), one of which is a RCT (Reed et al. 2019). The included studies covered 6 population groups:

All published evidence is on the single-lead KardiaMobile device.

For full details of the clinical evidence, see section 4 of the assessment report.

Evidence shows that monitoring with KardiaMobile increases AF detection

3.3 Three RCTs including 1 UK trial (Koh et al. 2021; Goldenthal et al. 2019; Reed et al. 2019) found that significantly more people in the KardiaMobile monitored group had AF detected compared with those who had standard care, which included 24-hour Holter monitoring. This was supported by the results from an observational study (Yan et al. 2020).

Evidence suggests that the KardiaMobile algorithm has a high diagnostic accuracy per electrocardiogram (ECG) recording

3.4 Four peer reviewed studies (Hermans et al. 2021; Selder et al. 2019; William et al. 2018; Lowres et al. 2016) reported on diagnostic accuracy of AF detection using the KardiaMobile algorithm compared with clinical interpretation of the KardiaMobile ECG as the reference standard. Its sensitivity ranged between 92% and 99% per recorded ECG, with a specificity between 92% and 98%. However, the external assessment centre (EAC) highlighted that diagnostic accuracy was reported on a per ECG recording and not a per person basis. Also, these 4 studies had 4 different patient populations with a pre-test probability of AF between 4.8% and 35.6%.

Evidence shows that using KardiaMobile reduces time to AF detection but there is no direct evidence for clinical outcomes after AF diagnosis

3.5 Reed et al. 2019 showed that people using KardiaMobile had their symptomatic cardiac arrhythmia detected significantly earlier than those having standard care (9.9 days compared with 48.0 days, p=0.0004). This finding was supported by 1 observational study (Yan et al. 2020) which also reported that KardiaMobile significantly reduced the time to AF detection when compared with standard care. There was no direct published evidence to show that using KardiaMobile improves clinical outcomes (such as reduction in stroke) after a diagnosis of AF.

KardiaMobile is easy to use and is associated with an improvement in quality of life

3.6 The evidence from 12 studies and a patient survey reported that KardiaMobile was easier to use compared with other ECG monitors such as Holter monitors. People felt that KardiaMobile would be useful in self-monitoring at home and improving their ability to access the care they needed. Two RCTs (Caceres et al. 2020 and Guhl et al. 2020) showed that people who used KardiaMobile had a significant improvement in AF-specific quality of life scores compared with people in the control groups. The EAC noted that both trials used additional interventions, and the effect of KardiaMobile alone on quality of life may be difficult to interpret.

The rate of unclassified ECG outputs varied in the studies but is falling because of software updates

3.7 Evidence reported that there were a proportion of ECG traces that do not fit the current KardiaMobile algorithm classifications, ranging from 9.6% to 27.6%. These outputs are presented as unclassified. However, software updates are improving the classification algorithm, and the number of unclassified outputs is reducing. Also, around 0.6% to 1.9% of KardiaMobile outputs were unreadable. This often happens when an ECG trace has interference and cannot be interpreted by the Kardia app, however a proportion of these can be interpreted by a clinician.

Published cost evidence includes 2 UK studies representing NHS costs

3.8 Three published studies reported the economic impact of KardiaMobile

All studies reported that KardiaMobile was cost saving. Two studies reported that the main driver for the saving was a reduction in healthcare appointments.

The company presented a cost model showing that monitoring with KardiaMobile saves between £320 and £382 per person over 5 years

3.9 The company developed a de novo model comparing KardiaMobile with Holter monitoring and the Zio patch. The model included people aged 64 or above with known or suspected AF who were referred for ambulatory ECG monitoring in a secondary care setting. The model assessed the costs associated with diagnosing and managing AF. Overall, the company's base case showed that using KardiaMobile could save between £320 and £380 per person over 5 years because of the cost of the technology, reductions in repeat testing, referrals to secondary care, and stroke events.

For full details of the cost evidence, see section 9 of the assessment report.

The EAC was unable to validate the company model limiting the certainty in the results presented

3.10 The EAC was unable to validate the company's model and highlighted limitations and errors in some of the parameters and assumptions used. The complexity of the model meant that inconsistencies could not be investigated and corrected. There was a lack of robust evidence to support the need for such complex time dependencies in the diagnostic phase of the model, and this approach required several additional assumptions. The EAC considered the diagnosis phase could have been modelled more simply. Overall, the EAC considered the model to be overly complex, not transparent and not verifiable. It also did not agree with underlying structural assumptions, parameter choice or their implementation in the model.

The EAC's cost calculator finds KardiaMobile to be cost saving in some scenarios

3.11 The EAC developed a simple cost calculator to explore the expected costs of using KardiaMobile to detect and treat AF over a 1-year time horizon. The cost calculator is a decision tree based on AF detection rates and risk of stroke in treated and untreated AF. The EAC presented 6 scenarios that were informed by the published comparative studies, with varying populations, definition of standard care and AF detection rates (Hermans et al. 2021, Narasimha et al. 2018, Koh et al. 2021, Reed et al. 2019, Goldenthal et al. 2019, Hickey et al. 2017). KardiaMobile was found to be cost saving compared with Holter or external loop recording in 3 scenarios, with a saving of £144 to £490 per person. But KardiaMobile incurred an additional £32 per person when used in addition to standard care (including Holter monitoring).